There are available various biometric systems that use the human inherent physiological or behavioral characteristics for personal identification. The biometric system works on physical characteristics and behavioral characteristics kind. Physical characteristics are innate, congenital human physical characteristics, such as fingerprints, iris, hand geometry; face, etc. have genetic virtue. Behavior is performed from a person's motion features extracted, mostly acquired, such as handwriting, keystroke, gait, etc. Biometrics is considered as one of the technologies that changed the world and as expected in the near future, biometric authentication technology will delve into all aspects of our lives, and its influence will be integrated as much as the Internet.
Everyone has a biometric different from others in uniqueness and stability in the same period of time, not easy to forgery and counterfeiting, so the use of biometrics for identity finds a safe, reliable, and accurate. However, no single biometric is perfect, and various biometric identification methods have its certain scope and requirements of a single biometric system in practical applications show their limitations. The reliability of fingerprint identification is relatively high but requires actual physical contact; human face and iris recognition does not require physical contact, but in practice theft applications are subject to more environmental restrictions. Studies have shown that using gelatin fake finger can easily fool the fingerprint recognition system, the iris of people suffering from cataracts will change. With the continuous criminal means, intelligent, science and technology, the biometric system has security challenges.
In comparison with biometric identification method an EEG identification method is a relatively new idea. In fact, as early as in 1960s, neurophysiologists and psychiatrists proposed and validated “human EEG carries genetic information and the existence of a correlation between them”. However, most of the early research is committed to pathological analysis and clinical diagnostics; until recent years, researchers have only put more energy into the health of the human body, trying to establish some sort of individual characteristics and their EEG carries genetic information which will serve as an effective EEG characteristic for identification.
It is proven that the EEG signals based biometric systems can achieve a certain accuracy and faster speed, and does not produce any harm to the human body. Since EEG signals are basically the brain's thinking activity, it is difficult to counterfeit. The human brain EEG analysis shows that different individuals in different brain regions will produce different neural impulse response and hence it is individual specific.
However, there is a common problem with normal EEG data processing. EEG usually reflects thousands of simultaneous on-going brain processes. EEG is still interpreted as two dimensional in clinical practice plotting voltage values in relation to time which does not reveal any unique quality of an individual. The brain response to a single stimulus or event of interest is not usually visible in the EEG recording of a single trial. In order to view the brain response to a specific stimulation applied to a subject under study, multiple trials (i.e. 100 trials or more) must be conducted to cause random brain activity to be averaged out and hence to remain the relevant ERP. Furthermore, multi-trial averaging contributes to loss of distinctive physiological information, which may prove useful for individual identification, disease diagnosis, and other fields of study such as psychology and pharmaceuticals. As such, an estimation scheme based on a single trial, which uses new signal processing EEG techniques, minimizes the information loss and reduces ERP or EP recording time is highly desirable.
The importance of accurate, timely detection of brain activity is crucial if to be used in identification and authentication. However, most mental and neurological states are evaluated mainly through interviews and subjective exams based on the subjects' temporary performance at that time. There is no objective quantitative test for evaluating baseline brain function. Imaging technologies such as standard magnetic resonance imaging (MRI) show only structure within the brain without providing an indication of dynamic brain function. Magnetic resonance spectroscopy and functional MRI provide functional status of different regions of brain but their use is very limited due to high cost, non-portability and limited availability of machines. EEG is the most effective, accurate and cheap method for evaluating brain function, but interpretation requires interpretation of multichannel graphs based on visual analysis by highly trained experts. Moreover current interpretation of EEG by expert does not provide any individual identification or early disease prediction due to lack of use of advanced signal processing techniques.
The present invention overcomes the above shortcomings by providing a novel methodology, which will be helping in diagnosing the different brain activity more accurately and comprehensibly as a brain signature. The matrix formed using EEG data will characterize the unique features of an individual, which can be used as person identification and assessment.